https://everymac.com/systems/apple/macbook_pro/specs/macbook...
I can think of things like 4K video editing or 3D rendering but as a software engineer is there anything we really need to spend the extra money on an MBP for?
I'm currently on a M1 Max but am seriously considering switching to an MBA in the next year or two.
A lot of software dev workflows often require running some number of VMs and containers, if this is you the chances of hitting that thermal throttle are not insignificant. When throttling under load occurs it’s like the machine suddenly halves in performance. I was working with a mess of micro services in 10-12 containers and eventually it just got too frustrating.
I still think these MBAs are superb for most people. As much as I love a solid state fanless design, I will for now continue to buy Macs with active cooling for development work. It’s my default recommendation anytime friends or relatives ask me which computer to buy and I still have one for light personal use.
More seriously though I agree it depends on workload. If you've got a dev flow that hits the resources in spikes (like initial builds that then flatten off to incremental) it works pretty well with said occasional breaks but if your setup is just continuously hammering resources it would be less than ideal.
Airs are good for the general use case but some development (rust, C++) really eat cores and memory like nothing else.
That does seem to fit the bill though of being more of a niche use case for which MBPs will be best suited for going forward.
Seems like most devs who are not on rust/c++ projects will be just fine with an Air equipped with enough memory.
Local software development (node/TS). When opus-4.6-fast launched, it felt like some of the limiting factor in turnaround time moved from inference to the validation steps, i.e. execute tests, run linter, etc. Granted, that's with endpoint management slowing down I/O, and hopefully tsgo and some eslint replacement will speed things up significantly over there.
A) Embeddings.
B) Things like classification, structured outputs, image labelling etc.
C) Image generation.
D) LLM chatbot for answering questions, improving email drafts etc.
E) Agentic coding.
?
I have a MBP with M1 Max and 32GB RAM. I can run a 20GB mlx_vlm model like mlx-community/Qwen3.5-35B-A3B-4bit. But:
- it's not very fast
- the context window is small
- it's not useful for agentic coding
I asked "What was mary j blige's first album?" and it output 332 tokens (mostly reasoning) and the correct answer.
mlx_vlm reported:
Prompt: 20 tokens @ 28.5 t/s | Generation: 332 tokens @ 56.0 t/s | Peak memory: 21.67 GBIt could also be possible that compsci kids have a powerful desktop at home, or are more savvy with university cloud computing, for any edge cases or computationally expensive tasks.
The MacBook Air has ~16 GiB RAM. The Desktop has 128 GiB, and a lot more processing power and disk space.
I’m not sure why this happens or who formulates these recommendations, but I’ve seen it before with students in fields that just don’t do much heavy duty computation or video editing being told to buy laptops with top-of-the-line specs.
It's the same at work, to some degree. Our in-house ERP software performs like kicking a sack of rocks down a hill. I don't know how often I had to show devs that the hardware is actually idle and they're mostly derailing themselves with DB table locks, GC issues and whatnot. If I weren't pushing back, we probably would have bought the biggest VMs just to let them sit idle.